Dr David Cornforth

Career Summary

Biography

Dr David Cornforth is a Senior Lecturer in Information Technology at the University of Newcastle. His research interests include data mining, artificial life and computer assisted medical diagnosis. He has conducted research in a range of areas – from synchronous and asynchronous processes in multi-agent systems, to intrusion detection systems for computer networks. He has successfully secured funding for his various projects including the Faculty Strategic Initiative Research Fund for his work in health informatics, and the New Staff Grant for his work in supervised and unsupervised data mining. Dr Cornforth also brings with him the valuable expertise acquired during his time as a CSIRO research scientist in renewable energy. He currently teaches into the courses related to video games design, application programming and mobile apps development. He is a convenor of the Applied Informatics Research Group (AIR).

Research ExpertiseFeature selection for supervised and unsupervised data mining: Feature selection to improve automated classification is well established, but feature selection for clustering is more problematic, because there is no "right' answer. Partition of a data set into clusters arises from various motivations. However, numerical measures of cluster quality can be used is association with search algorithms, including evolutionary methods.

Analysis of Heart Rate Variability using Multi Spectral Entropy Measures: Heart rate can be measured with simple equipment and does not provide the full ECG signal. However, there is much information in the beat to beat (RR) interval. Heart rate variability has been analysed with time- and frequency-domain methods, but more recent nonlinear analysis has shown an increased sensitivity for identifying risk of future morbidity and mortality in cardiac patients. Models of social networks: This project investigated the impact of media on shared opinion within social groups, using network models. My contribution was in experimental design and analysis of results (Stocker et al., 2002a & 2002b; Stocker et al., 2003). This work showed that different network topologies (random, scale-free, small-world, and regular) affect the spread of ideas through a social network.Agent-based artificial markets: This project simulates a network of intelligent software agents trading with one another to form stable solutions. Agents are self-interested and have diverse goals, yet a network of peers with no designated leader is able to cooperate and find solutions. I observed that cooperation is easier to achieve in heterogeneous agents, because this allows tie breaking (Cornforth et al., 2004). I proposed and studied the use of an evolutionary algorithm to optimise the agents, providing better solutions in dynamic problems (Cornforth, 2007). Asynchrony in network models: I have researched different methods of asynchrony in multi agent models, implemented them and performed experiments using network measures such as degree distribution, clustering coefficient, state entropy and Lyapunov exponents. I have identified several types of updating schemes, and modeled the effects of these schemes using one-dimensional cellular automata. Results showed that the scheme chosen provides very different dynamics. I invented the self-synchronous scheme, where agents are coupled with their neighbours, so can update their state in synchrony with their neighbours. I showed that it is possible to switch between chaotic, cyclic and modular behaviour by varying a single parameter. The significance of this is a possible mechanism by which environmental parameters influence emergent structure. (Cornforth et al., 2005; Cornforth et al., 2002; Cornforth et al., 2001). Some of my software is online at VLAB, Monash University’s Complexity Virtual Lab, hosted at http://journal-ci.csse.monash.edu.au/vlab/ (e.g. Self Synchronous under Cellular Automata).

Wrapper subset evaluation: This work aimed to determine the best set of measures for the automated classification of medical images. It is known that using too many measures has a detrimental effect upon the classifier performance. Although Principal Components Analysis is well established, it assumes a Gaussian distribution, whereas many modern classifier algorithms can model much more complicated and even concave distributions.

Teaching ExpertiseI have a record of excellence in teaching, which is demonstrated by the results of student surveys. I am committed to improving the quality of the student experience, which led me complete a Graduate Certificate in University Learning and Teaching, awarded by UNSW in 2007. I have adopted the practice of keeping a teaching portfolio, where I record my reflection on my teaching practice. I have developed a culture of scholarship in teaching, demonstrated by innovations that have improved the learning experience for my students, based on the application of relevant literature on teaching and learning. Through introducing different teaching styles and techniques during lectures, I am committed to generating a richer student-centred learning environment. My teaching experience includes: - Computer Tools for Engineers - Introduction to Computer Science - Programming Fundamentals - Introduction to Research in IT - Knowledge Based Systems - XML Technologies - Soft Computing - Research Methods - Data Analysis for Postgraduates I have had 10 years university teaching experience in three different universities and very diverse schools. I have taught service courses for other schools in face-to-face mode and distance learning, with a highly diverse student cohort in multidisciplinary schools. I have transformed the teaching of my courses by introducing many student-centred exercises, and adapting the course to serve the needs of off-campus students.

Administrative ExpertiseI have had an active and leadership role in the Applied Informatics Research (AIR) group.

In-match player performance, measured by data from Geographical Positioning System (GPS) devices, was predicted with a correlation coefficient of greater than 0.7. Predictions wer... [more]

In-match player performance, measured by data from Geographical Positioning System (GPS) devices, was predicted with a correlation coefficient of greater than 0.7. Predictions were based on heart rate variability measures and used advanced regression techniques based on machine learning. These techniques included methods for the selection of variables to be included in the regression study. Results indicate that variable selection using a wrapper subset method with a genetic algorithm outperformed both principal components analysis and the default method of using all variables. The success of prediction of match performance suggests a potential for new tools to assist the team coach in player selection and management of player training. This work also provides the possibility for a training programme to be adjusted specifically to meet the challenges of the size of the playing field and the temperature likely to be encountered on the day of the match.

Background: Physiological interactions are abundant within, and between, body systems. These interactions may evolve into discrete states during pathophysiological processes resul... [more]

Background: Physiological interactions are abundant within, and between, body systems. These interactions may evolve into discrete states during pathophysiological processes resulting from common mechanisms. An association between arterial stenosis, identified by low ankle-brachial pressure index (ABPI) and cardiovascular disease (CVD) as been reported. Whether an association between vascular calcification-characterized by high ABPI and a different pathophysiology-is similarly associated with CVD, has not been established. The current study aims to investigate the association between ABPI, and cardiac rhythm, as an indicator of cardiovascular health and functionality, utilizing heart rate variability (HRV). Methods and Results: Two hundred and thirty six patients underwent ABPI assessment. Standard time and frequency domain, and non-linear HRV measures were determined from 5-min electrocardiogram. ABPI data were divided into normal (n = 101), low (n = 67) and high (n = 66) and compared to HRV measures.(DFAa1 and SampEn were significantly different between the low ABPI, high ABPI and control groups (p < 0.05). Conclusion: A possible coupling between arterial stenosis and vascular calcification with decreased and increased HRV respectively was observed. Our results suggest a model for interpreting the relationship between vascular pathophysiology and cardiac rhythm. The cardiovascular system may be viewed as a complex system comprising a number of interacting subsystems. These cardiac and vascular subsystems/networks may be coupled and undergo transitions in response to internal or external perturbations. From a clinical perspective, the significantly increased sample entropy compared to the normal ABPI group and the decreased and increased complex correlation properties measured by DFA for the low and high ABPI groups respectively, may be useful indicators that a more holistic treatment approach in line with this more complex clinical picture is required.

With the large and growing user base of social media, it is not an easy feat to identify potential customers for business. This is mainly due to the challenge of extracting commer... [more]

With the large and growing user base of social media, it is not an easy feat to identify potential customers for business. This is mainly due to the challenge of extracting commercially viable contents from the vast amount of free-form conversations. In this paper, we analyse the Twitter content of an account owner and its list of followers through various text mining methods and segment the list of followers via an index. We have termed this index as the High-Value Social Audience (HVSA) index. This HVSA index enables a company or organisation to devise their marketing and engagement plan according to available resources, so that a high-value social audience can potentially be transformed to customers, and hence improve the return on investment.

One focus of multi-agent systems research is the notion that complex outcomes or behaviours may be arrived at through the interaction of agents. However, it is still an open quest... [more]

One focus of multi-agent systems research is the notion that complex outcomes or behaviours may be arrived at through the interaction of agents. However, it is still an open question as to how agents in a complex system form coalitions or modules, and how these coalitions self-organize into hierarchies. In this paper, we begin to address this question by investigating agent collaboration in the context of a high-level pattern recognition task. We propose a novel market-based communication protocol, which governs the aggregate behaviour of individual agents and subsequent emergent properties of the system. Based on the Contract Net Protocol, individual agents bid to join coalitions (or solutions to a given problem). An important contribution of this study is the analysis of the role heterogeneous agents play in the formation of coalitions. Using a simple model, we show that by promoting diversity within the agent population it is possible to avoid deadlock or "tie" conditions, which otherwise have to be solved arbitrarily by the deadlocked agents.